This section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present disclosure that are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
The dynamic range is, by definition, the ratio between the largest and smallest possible value of the changeable quantity corresponding to the luminance, which is a photometric measure of the luminous intensity per unit area of light travelling in a given direction (luminance being expressed in cd/m2).
Different techniques have been proposed in order to generate an image/video with a higher dynamic range from an image/video with a lower dynamic range.
A first known technique relies on the use more sensitive sensors (such as RED or HARRI cameras), multi camera setup (using a stereo rig with no parallax and one over exposed and one under exposed camera) or temporal bracketing (shooting the same image successively with different apertures) to capture directly HDR image/video.
However, such devices are very expensive and the captured HDR images need to be converted in lower dynamic range images to be encoded and transmitted.
In addition, the temporal bracketing is mainly used for photos/static images because of the motion blur created for video.
A second known technique relies on manual creation. During the color management process, it is possible to extend the colors and brightness of an image based on the rendering on a reference display. Such technique is a traditional post-processing performed for movies.
However, such technique is time-consuming and does not generate an image with a natural light.
A third known technique relies on inverse Tone Mapping Operators (iTMO). ITMO are used to extend the dynamic from one range to a higher one. Such operators can be classified based on image processing algorithms used, such as global operators, where the same expansion function is used for all pixels, or local operators where the expansion function varies depending on the content.
However, such operators are not fully efficient today since it is difficult to obtain a realistic HDR video, with local variations, consistent temporally. This is a general problem when trying to extrapolate information from a reduce set of data.
The present disclosure overcomes at least one of the above-mentioned shortcomings.